Tropical Forest Landscape Fragmentation in Batang Toru Watershed, North Sumatra

Timber-based forest management is now shifting to as broader scope including ecosystem-based management. Timber-oriented forest management frequently affects the fragmentation of forest landscape. This paper defines the degree of forest landscape fragmentation in Batang Toru watershed, North Sumatra through indentification of correlation between forest landscape fragmentation and driving factors including biophysical and anthropogenic factors. Identification structure, pattern, and fragmentation of forest landscape were performed using Landsat imageries acquired in 1989, 2001, and 2013. Forest and land cover classes were analyzed using FRAGSTAT 3.3 to generate landscape metrics. Fragmentation of forest landscape was identified using landscape metrics, i.e., area, patch density, number of patch, contiguity and proximity index.  The clumpiness index of landscape metrics describes the pattern of forest landscape, while the patch size proportions expressed structure of forest landscape.  This study found that forest landscapes located in downstream of the watershed show more fragmented than area in the upper stream, while the sub-watershed of Batang Toru Hilir is more clumped  than the others. This study concludes that (1) the forest landscape fragmentation tend to increase since 1989 to 2013; and (2) the degree of forest landscape fragmentation has close correlation with the distance to main road and river.

Tropical Forest Landscape Fragmentation in Batang Toru Watershed, North Sumatra

Timber-based forest management is now shifting to as broader scope including ecosystem-based management. Timber-oriented forest management frequently affects the fragmentation of forest landscape. This paper defines the degree of forest landscape fragmentation in Batang Toru watershed, North Sumatra through indentification of correlation between forest landscape fragmentation and driving factors including biophysical and anthropogenic factors. Identification structure, pattern, and fragmentation of forest landscape were performed using Landsat imageries acquired in 1989, 2001, and 2013. Forest and land cover classes were analyzed using FRAGSTAT 3.3 to generate landscape metrics. Fragmentation of forest landscape was identified using landscape metrics, i.e., area, patch density, number of patch, contiguity and proximity index.  The clumpiness index of landscape metrics describes the pattern of forest landscape, while the patch size proportions expressed structure of forest landscape.  This study found that forest landscapes located in downstream of the watershed show more fragmented than area in the upper stream, while the sub-watershed of Batang Toru Hilir is more clumped  than the others. This study concludes that (1) the forest landscape fragmentation tend to increase since 1989 to 2013; and (2) the degree of forest landscape fragmentation has close correlation with the distance to main road and river.

Scenario analysis for integrated water resources management under future land use change in the Urmia Lake region, Iran

Arid and semi-arid regions are particularly vulnerable to global environmental change because of their fragile climatic conditions. The rapid development of land use is expected to affect aquatic ecosystems in these regions. In this study, we focused on how land use change affects the stream flow and inflow to Urmia Lake in the Mordagh Chay basin, Iran. This case-study exemplifies dynamics found across a much larger region. We mapped changes in land use between 1993–2015 using satellite imagery and modeled future changes using the Dyna-CLUE model. We projected future land use change until 2030 under four scenarios: continuing of the current trend of water use, 40% water withdrawal reduction, and two other scenarios with 40% water withdrawal reduction and improvements of irrigation efficiency up to 50% and 85%. Between 1993–2015, 21% of the study area changed to orchard and arable land mostly at the cost of rangeland. However, upon reduction of water withdrawal our analyses showed that garden must decrease between 27% and 40%. Rainfed cropland is projected to experience a major increase in all scenarios, especially in the case of reduced water withdrawal, where it will increase by 217%. In order to achieve sustainable water resources management land use plays a major role and leads to different land use futures in this type of semi-arid regions

Using fractal analysis in modeling the dynamics of forest areas and economic impact assessment:Maramureș County, Romania, as a case study

This study uses fractal analysis to quantify the spatial changes of forest resources caused by an increase of deforested areas. The method introduced contributes to the evaluation of forest resources being under significant pressure from anthropogenic activities. The pressure on the forest resources has been analyzed for Maramureș County, one of the most deforested counties in Romania. In order to evaluate this, the deforested areas were calculated for the period of 2001–2014, by using the Global Forest Change 2000–2014 database. The Fractal Fragmentation Index (FFI) and Fixed Grid 2D Lacunarity (FG2DL) were used to quantify the degree of fragmentation and dispersion of the forested areas, and thereby the extent to which a forest area is affected by deforestation. The process of quantifying the pressure on forested areas included the creation of a database for the period of 2000–2014 containing economic activities (turnover) related to woody recourses, important indicators of forest exploitation. Taken together, the results obtained indicate a dramatic increase in deforested areas (over 19,122 ha in total for the period of analysis), in Maramureș County

Assessment of textural differentiations in forest resources in Romania using fractal analysis

Deforestation and forest degradation have several negative effects on the environment including a loss of species habitats, disturbance of the water cycle and reduced ability to retain CO2, with consequences for global warming. We investigated the evolution of forest resources from development regions in Romania affected by both deforestation and reforestation using a non-Euclidean method based on fractal analysis. We calculated four fractal dimensions of forest areas: the fractal box-counting dimension of the forest areas, the fractal box-counting dimension of the dilated forest areas, the fractal dilation dimension and the box-counting dimension of the border of the dilated forest areas. Fractal analysis revealed morpho-structural and textural differentiations of forested, deforested and reforested areas in development regions with dominant mountain relief and high hills (more forested and compact organization) in comparison to the development regions dominated by plains or low hills (less forested, more fragmented with small and isolated clusters). Our analysis used the fractal analysis that has the advantage of analyzing the entire image, rather than studying local information, thereby enabling quantification of the uniformity, fragmentation, heterogeneity and homogeneity of forests

A Random Forest-Cellular Automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales

This paper explores potential future land use/cover (LUC) dynamics in the Attica region, Greece, under three distinct economic performance scenarios. During the last decades, Attica underwent a significant and predominantly unregulated process of urban growth, due to a substantial increase in housing demand coupled with limited land use planning controls. However, the recent financial crisis affected urban growth trends considerably. This paper uses the observed LUC trends between 1991 and 2016 to sketch three divergent future scenarios of economic development. The observed LUC trends are then analysed using 27 dynamic, biophysical, socio-economic, terrain and proximity-based factors, to generate transition potential maps, implementing a Random Forests (RF) regression modelling approach. Scenarios are projected to 2040 by implementing a spatially explicit Cellular Automata (CA) model. The resulting maps are subjected to a multiple resolution sensitivity analysis to assess the effect of spatial resolution of the input data to the model outputs. Findings show that, under the current setting of an underdeveloped land use planning apparatus, a long-term scenario of high economic growth will increase built-up surfaces in the region by almost 24%, accompanied by a notable decrease in natural areas and cropland. Interestingly, in the case that the currently negative economic growth rates persist, artificial surfaces in the region are still expected to increase by approximately 7.5% by 2040

Environmental impacts of the U.S.-Mexico avocado supply chain

The U.S. imports 87 percent of its avocados from one region (Michoacán) in Mexico. Although environmental and social costs associated with avocado production are significant, consumers and retailers in the U.S. are not aware of them in part due to complex, opaque supply chains. In this paper, we use a methodology known as TRACAST (Tracking Corporate Actors Across Space and Time) to reconstruct avocado supply chains between U.S. retailers (e.g. Kroger and Costco) and Mexican producers and exporters. Using remote sensing and machine learning, we document how avocado plantations have led to significant deforestation in Michoacán, whose forests are important reservoirs for biodiversity, especially the Monarch butterfly (Danaus plexippus). We estimate that ~20% of the total forest loss (15,000 ha) in Michoacán between 2001 and 2017 is associated with expansion of avocado orchards. Despite these impacts, interviews reveal that industry experts (namely representatives of firms and government officers) do not consider avocado production to be a driver of deforestation in the region. This disconnection between actual and perceived environmental impact can be addressed by the U.S. governmental agencies (namely USDA APHIS) who play influential roles in regulating avocado imports for sanitary and health purposes and by the vertically integrated avocado trading companies who connect Michoacán packing houses to Kroger, Costco, and other large U.S. grocery retailers. Key measures to make the U.S.- Mexico avocado trade more sustainable include greater information transparency and multi-stakeholder initiatives.Master of ScienceSchool for Environment and SustainabilityUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/154993/1/Cho_Kimin_Thesis.pd

Monitoring and modelling disturbances to the Niger Delta mangrove forests

The Niger River Delta provides numerous ecosystem services (ES) to local populations and holds a wealth of biodiversity. Nevertheless, they are under threat of degradation and loss mainly due to the population increase and oil and gas extraction activities. Monitoring mangrove vegetation change and understanding the dynamics related with these changes is crucial for the short and longer-term sustainability of the Niger Delta Region (NDR) and its mangrove forests. Over the last two decades, open access remote sensing data, together with technological and algorithmic advancements, have provided the ability to monitor land cover over large areas through space and time. However, the analysis of land cover dynamics over the NDR using freely available optical remote sensing data, such as Landsat, remains challenging due to the gaps in the archive associated with the West African region and the issue of cloud contamination over the wet tropics. This thesis applies state-art-of-the-art remote sensing techniques and integrated modelling approaches to provide reliable information relating to monitoring and modelling of land cover change in the NDR, focusing on its mangrove forests. Spectral-temporal metrics from all available Landsat images were used to accurately map land cover in three time points, using a Random Forests machine learning classification model. The performance of the classification was tested when L-band radar data are added to the Landsat-based metrics. Results showed that Landsat based metrics are sufficient in mapping land cover over the study region with high overall classification accuracies over the three time points (1988, 2000, and 2013) and degraded mangroves were accurately mapped for the first time. Two additional assessments: a change intensity analysis for the entire NDR and, fragmentation analysis focusing on mangrove land cover classes were carried out for the first time ever. The drivers of mangrove degradation were assessed using a Multi-layer Perceptron, Artificial Neutral Networks (MLP-ANN) algorithm. The results reveal that built-up infrastructure variables were the most important drivers of mangrove degradation between 1988 and 2000, whilst oil and gas infrastructure variables were the most important drivers between 2000 and 2013. Results also show that population density was the least important driver of mangrove degradation over the two study periods. Future land cover changes and mangrove degradation were predicted under two business-as-usual scenarios in the short (2026) and longer-term (2038) using a Multi-Layer Perceptron neutral network and Markov chain (MLP-ANN+MC) model. The model’s accuracy was assessed using the highly-accurate land cover classification of 2013. Results show that that mangrove forest and woodlands (lowland and freshwater forests) are demonstrating a net loss, whilst the built-up areas and agriculture are indicating a net increase in both the short and longer-term scenarios. However, degraded mangroves are demonstrating a net increase in the short-term scenario. Interestingly, in the longer-term scenario, more than double the net increase of mangroves degraded in the short-term scenario, are predicted to recover to their healthier state. The thesis results could provide useful information for planning conservation measures for sustainable mangrove forest management of the entire NDR